Accepting applications until: 16 October 2026Job DescriptionYour New RoleMLOps EngineerGlobal: IQ is the team building our new intelligence platform, turning first-party and partner data into smarter, data-led media plans across Global's audio and Outdoor inventory. As a MLOps Engineer at Global, you'll build the operational infrastructure that brings AI and ML models into production.
The following information provides an overview of the skills, qualities, and qualifications needed for this role.
You'll own the platforms, pipelines and processes that let our Data Science teams deploy, monitor, retrain and govern models reliably at scale—from the ground up.
Key Responsibilities
ML Infrastructure & Deployment (40%): Build automated pipelines for model training, validation and deployment, plus model registries, feature stores and inference services, with self-serve tooling for Data Science teams. Model Monitoring & Operations (30%): Implement monitoring, alerting and automated recovery for ML workloads—covering latency, data quality and drift—and own rollback, rollout and incident response. MLOps Governance & Best Practice (20%): Establish controls for model lineage, reproducibility and audit trails, and introduce ML-specific CI/CD, testing and release automation.
Collaboration & Enablement (10%): Partner with Data Science, Data Engineering and Product, and mentor junior engineers to raise operational standards.
What you will love about this role: Think Big: This is a true AI-driven product—ML isn't a feature, it's the product, and your infrastructure directly enables business value.
Own It: You're not maintaining legacy systems—you're establishing the MLOps patterns and standards that will scale for years.
Keep it Simple: You'll build pragmatic, reusable patterns that keep ML systems reliable and maintainable without over-engineering.
Better Together: Global: IQ is a tight collaboration between technical and commercial teams. What Success Looks LikeIn your first few months, you'll have: Defined a clear operating model between MLOps and the teams developing models.
Delivered an end-to-end MLOps path for at least one production use case, from model handoff through deployment, monitoring and rollback.
Established baseline standards for model versioning, environment management and deployment.
Implemented monitoring and alerting across operational health, data quality and model performance. What You'll NeedMLOps experience: You've operationalised ML models in production, owning deployment, monitoring and lifecycle management.
Strong programming: Production-quality, testable Python.
Cloud expertise: Deep AWS knowledge (SageMaker, Lambda, ECS/EKS, Step Functions); Snowflake a plus. MLOps tooling: Experiment tracking and registries, workflow orchestration, model serving and feature stores. CI/CD & IaC: ML-specific CI/CD, Terraform, Docker and test automation. xohmjla Cross-disciplinary communication: You translate between Data Science and Engineering and explain trade-offs to any audience.
SummaryLocation: Holborn
- LondonType: Full time
ML Ops Engineer in Vauxhall employer: Global
Global is an exceptional employer that champions inclusivity and creativity, making it a fantastic place for aspiring Junior Shooters & Video Editors. Located in the vibrant Leicester Square, employees benefit from a dynamic work culture that encourages collaboration and innovation, with ample opportunities for professional growth within a highly skilled team. With the chance to create impactful content seen by millions, Global not only values your contributions but also supports your development in a fast-paced, exciting environment.
